diff --git a/NEWS.md b/NEWS.md
index 408d8e3..ee92a90 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -71,6 +71,27 @@
 
 ## Breaking changes
 
+* **`matchData()`'s arguments are now `dat` and `source`**, replacing
+  `speciesDat` and `envDat`.
+
+  The function was never specific to species observations or environmental data.
+  It is a spatiotemporal nearest-feature join between two `sf` point objects, and
+  works as well for tag positions against a model field, moorings against
+  satellite retrievals, or one gridded product against another. The old names
+  described one use of it as though it were the only one.
+
+  **The old names still work**, with a warning, so existing scripts and
+  `taupatch` keep running. They will be removed in a later version.
+
+  ```r
+  matchData(observations, env)                 # positional, unchanged
+  matchData(dat = observations, source = env)  # new names
+  matchData(speciesDat = obs, envDat = env)    # still works, warns
+  ```
+
+  One related change: a `source` column whose name collides with one already in
+  `dat` is now suffixed **`.matched`** rather than `.env`, for the same reason.
+
 * **The `BigelowLab/copernicus` dependency is gone.** It was used in two places,
   both in `accessEnvDat()`, and both are now internal. This removes the
   `Remotes:` field and the hand-created `~/.copernicusdata` file that a new
diff --git a/R/accessEnvDat.R b/R/accessEnvDat.R
index 3eb5e46..8330aed 100644
--- a/R/accessEnvDat.R
+++ b/R/accessEnvDat.R
@@ -347,7 +347,7 @@ read_day <- function(item, vars) {
 #' @param n_workers <integer> how many days to download at once. See the
 #'   Downloading in parallel section. Use `n_workers = 1` to download one day at
 #'   a time.
-#' @return envDat <sf object> sf object containing requested environmental data from Copernicus Marine Service
+#' @return <sf object> sf object containing requested environmental data from Copernicus Marine Service
 #' @export
 accessEnvDat <- function(product_id = NULL, dataset_id = NULL, vars,
                          years = NULL, months = NULL,
diff --git a/R/matchData.R b/R/matchData.R
index cc126f4..695c676 100644
--- a/R/matchData.R
+++ b/R/matchData.R
@@ -1,108 +1,158 @@
 
-#' Match environmental data to species occurrence data
+#' Match one set of points to another in space and time
 #'
-#' Joins each species observation to the nearest environmental grid point within
-#' the same time period. The time period is the environmental data's own temporal
-#' resolution: daily products match on year/month/day, monthly products (e.g.
-#' Copernicus `...P1M-m` means) on year/month, and annual products on year alone.
+#' Joins each row of `dat` to the nearest feature of `source` within the same
+#' time period, and returns `dat` with `source`'s columns added.
 #'
-#' Matching at the environmental data's native resolution matters because a
-#' day-exact join against monthly data matches nothing - a monthly product carries
-#' one time step per month, while observations fall on arbitrary days.
+#' Neither side has to be species observations or environmental data. It is a
+#' spatiotemporal nearest-feature join between two `sf` point objects that carry
+#' `YEAR`/`MONTH`/`DAY` columns, so it works equally for stations against a
+#' covariate grid, tag positions against a model field, moorings against
+#' satellite retrievals, or one gridded product against another.
 #'
-#' @param speciesDat <sf object> species observation data (e.g., presence, density, count);
-#'                               must have spatial and temporal components. Needs
-#'                               year and month columns, plus a day column when
-#'                               matching at daily resolution.
-#' @param envDat <sf object> environmental data accessed using the datamatch::accessEnvDat
-#'                           function to be matched to the species observation data
-#' @param temporal_resolution <char> one of "auto" (default), "day", "month", or
-#'                                   "year". "auto" infers the resolution from
-#'                                   `envDat`'s own time steps.
-#' @return <sf object> `speciesDat` with the matched environmental variables joined on,
-#'                     one row per input observation, plus LON/LAT coordinate columns.
-#'                     Observations in a period with no environmental data get NA
-#'                     for the environmental variables, and a warning is issued.
-#'                     An environmental variable whose name collides with a column
-#'                     already in `speciesDat` is suffixed `.env`.
+#' @section Matching in time:
+#' The time period is `source`'s own resolution: daily data matches on
+#' year/month/day, monthly data (Copernicus `...P1M-m` means, say) on
+#' year/month, and annual data on year alone.
+#'
+#' That matters because a day-exact join against monthly data matches nothing. A
+#' monthly product carries one time step per month, while observations fall on
+#' arbitrary days. `temporal_resolution` overrides the inference when the data
+#' cannot speak for itself.
+#'
+#' @section What is preserved:
+#' One row out per row of `dat`, in the same order, whatever happens. A period
+#' `source` does not cover gives `NA` for its columns and a warning naming the
+#' periods, rather than dropping those rows — a silent change in row count is a
+#' worse outcome than a visible gap.
+#'
+#' `dat` keeps its own columns. One of `source`'s that collides with a name
+#' already in `dat` is suffixed `.matched`, so nothing of `dat`'s is overwritten
+#' or renamed.
+#'
+#' @param dat <sf object> the points to add columns to: observations, stations,
+#'   tag positions, anything with coordinates and time. Needs year and month
+#'   columns, plus a day column when matching at daily resolution. Columns whose
+#'   names begin with those words are recognised, so `Year` or `obs_month` work.
+#' @param source <sf object> the points to take values from, typically a grid
+#'   from [accessEnvDat()]. Must carry `YEAR`/`MONTH`/`DAY`.
+#' @param temporal_resolution <char> one of `"auto"` (default), `"day"`,
+#'   `"month"`, or `"year"`. `"auto"` uses the step `accessEnvDat()` recorded on
+#'   `source`, or infers it from `source`'s time steps.
+#' @param speciesDat,envDat deprecated names for `dat` and `source`. Still
+#'   accepted, with a warning.
+#' @return <sf object> `dat` with `source`'s columns joined on, one row per input
+#'   row, plus `LON`/`LAT` coordinate columns.
+#' @examples
+#' \dontrun{
+#' env <- accessEnvDat(vars = "SST", years = 2010, months = 1:12, bounding_box = bb)
+#'
+#' matched <- matchData(observations, env)
+#'
+#' # Chains, so several sources land on one table
+#' matched <- matchData(matched, chlorophyll)
+#' }
+#' @seealso [accessEnvDat()] for the usual `source`, [attach_bathymetry()] and
+#'   [attach_climate_index()] for covariates that are not matched this way
 #' @export
-matchData <- function(speciesDat, envDat,
-                      temporal_resolution = c("auto", "day", "month", "year")) {
+matchData <- function(dat, source,
+                      temporal_resolution = c("auto", "day", "month", "year"),
+                      speciesDat = NULL, envDat = NULL) {
+
+  # The old names were specific to one use of a function that was never specific
+  # to it. Accepted for now because taupatch and any script written against the
+  # old signature call them by name, and breaking those silently would be worse
+  # than carrying two lines.
+  if (!is.null(speciesDat)) {
+    warning("`speciesDat` is now `dat`. The old name still works but will be ",
+            "removed.", call. = FALSE)
+    if (missing(dat)) dat <- speciesDat
+  }
+  if (!is.null(envDat)) {
+    warning("`envDat` is now `source`. The old name still works but will be ",
+            "removed.", call. = FALSE)
+    if (missing(source)) source <- envDat
+  }
+  if (missing(dat) || missing(source)) {
+    if (is.null(speciesDat) || is.null(envDat)) {
+      stop("Both `dat` and `source` are required.", call. = FALSE)
+    }
+  }
 
   temporal_resolution <- match.arg(temporal_resolution)
   if (temporal_resolution == "auto") {
-    temporal_resolution <- detect_temporal_resolution(envDat)
+    temporal_resolution <- detect_temporal_resolution(source)
   }
   match_keys <- switch(temporal_resolution,
                        day = c("YEAR", "MONTH", "DAY"),
                        month = c("YEAR", "MONTH"),
                        year = "YEAR")
 
-  speciesDat <- standardize_time_columns(speciesDat, match_keys)
+  dat <- standardize_time_columns(dat, match_keys)
 
-  env_geom <- attr(envDat, "sf_column")
-  env_vars <- setdiff(names(envDat), c("YEAR", "MONTH", "DAY", env_geom))
+  source_geom <- attr(source, "sf_column")
+  source_vars <- setdiff(names(source), c("YEAR", "MONTH", "DAY", source_geom))
 
   # Both sides need a CRS before they can be reconciled. Without one,
   # st_transform() fails with "crs not found: is it missing?", which is true but
   # does not say which object or what to do. Silently assuming a CRS would be
   # worse: coordinates would be matched as though they were degrees, and every
-  # observation would join to whichever cell happened to be nearest in a
-  # meaningless space.
-  for (side in list(list(x = speciesDat, name = "speciesDat"),
-                    list(x = envDat, name = "envDat"))) {
+  # row would join to whichever feature happened to be nearest in a meaningless
+  # space.
+  for (side in list(list(x = dat, name = "dat"),
+                    list(x = source, name = "source"))) {
     if (is.na(sf::st_crs(side$x))) {
-      stop(side$name, " has no coordinate reference system, so it cannot be ",
-           "matched.\nSet one with sf::st_crs(", side$name,
+      stop("`", side$name, "` has no coordinate reference system, so it cannot ",
+           "be matched.\nSet one with sf::st_crs(", side$name,
            ") <- 4326 for longitude/latitude,\nor the EPSG code the ",
            "coordinates are actually in.", call. = FALSE)
     }
   }
 
-  envDat <- sf::st_transform(envDat, sf::st_crs(speciesDat))
+  source <- sf::st_transform(source, sf::st_crs(dat))
 
-  # Give environmental variables that share a name with a species column an
-  # explicit ".env" suffix. Otherwise st_join() disambiguates them as ".x"/".y",
-  # which both renames the species column and makes the result's column names
+  # Give a source column that shares a name with one in `dat` an explicit
+  # ".matched" suffix. Otherwise st_join() disambiguates them as ".x"/".y",
+  # which both renames the caller's column and makes the result's column names
   # depend on whether a given period actually matched anything.
-  collisions <- intersect(env_vars, names(speciesDat))
+  collisions <- intersect(source_vars, names(dat))
   if (length(collisions) > 0) {
-    renamed <- paste0(collisions, ".env")
-    names(envDat)[match(collisions, names(envDat))] <- renamed
-    env_vars[match(collisions, env_vars)] <- renamed
+    renamed <- paste0(collisions, ".matched")
+    names(source)[match(collisions, names(source))] <- renamed
+    source_vars[match(collisions, source_vars)] <- renamed
   }
 
-  periods <- unique(sf::st_drop_geometry(speciesDat)[match_keys])
+  periods <- unique(sf::st_drop_geometry(dat)[match_keys])
   matched <- vector("list", nrow(periods))
   unmatched_periods <- character()
 
   for (i in seq_len(nrow(periods))) {
-    in_period <- rep(TRUE, nrow(speciesDat))
-    env_in_period <- rep(TRUE, nrow(envDat))
+    in_period <- rep(TRUE, nrow(dat))
+    source_in_period <- rep(TRUE, nrow(source))
     for (key in match_keys) {
-      in_period <- in_period & speciesDat[[key]] == periods[[key]][i]
-      env_in_period <- env_in_period & envDat[[key]] == periods[[key]][i]
+      in_period <- in_period & dat[[key]] == periods[[key]][i]
+      source_in_period <- source_in_period & source[[key]] == periods[[key]][i]
     }
 
-    obs <- speciesDat[in_period, ]
-    env_slice <- envDat[env_in_period, c(env_vars)]
+    rows <- dat[in_period, ]
+    source_slice <- source[source_in_period, c(source_vars)]
 
-    if (nrow(env_slice) == 0) {
-      # st_nearest_feature cannot join against an empty set, so fill the
-      # environmental columns with NA rather than dropping the observations.
-      # Dropping them would silently change the row count of the result.
-      for (v in env_vars) obs[[v]] <- NA
+    if (nrow(source_slice) == 0) {
+      # st_nearest_feature cannot join against an empty set, so fill the matched
+      # columns with NA rather than dropping the rows. Dropping them would
+      # silently change the row count of the result.
+      for (v in source_vars) rows[[v]] <- NA
       unmatched_periods <- c(unmatched_periods,
                              paste(unlist(periods[i, , drop = TRUE]), collapse = "-"))
-      matched[[i]] <- obs
+      matched[[i]] <- rows
     } else {
-      matched[[i]] <- sf::st_join(obs, env_slice, join = sf::st_nearest_feature)
+      matched[[i]] <- sf::st_join(rows, source_slice, join = sf::st_nearest_feature)
     }
   }
 
   if (length(unmatched_periods) > 0) {
-    warning("No environmental data for ", length(unmatched_periods),
-            " period(s); environmental variables set to NA for: ",
+    warning("No data in `source` for ", length(unmatched_periods),
+            " period(s); matched columns set to NA for: ",
             paste(utils::head(unmatched_periods, 5), collapse = ", "),
             if (length(unmatched_periods) > 5) ", ..." else "", call. = FALSE)
   }
@@ -120,7 +170,7 @@ matchData <- function(speciesDat, envDat,
   matched_data
 }
 
-#' Infer the temporal resolution of environmental data
+#' Infer the temporal resolution of a set of time steps
 #'
 #' Reads the resolution off the time steps actually present: more than one day
 #' within any month means daily data, and more than one month within any year
@@ -135,21 +185,22 @@ matchData <- function(speciesDat, envDat,
 #' fine leaves them unmatched and warns. Pass `temporal_resolution` explicitly to
 #' override.
 #'
-#' @param envDat <sf object> environmental data with YEAR/MONTH/DAY columns
+#' @param x <sf object> an object with YEAR/MONTH/DAY columns, typically the
+#'   `source` side of a match
 #' @return one of "day", "month", or "year"
 #' @keywords internal
-detect_temporal_resolution <- function(envDat) {
+detect_temporal_resolution <- function(x) {
   # accessEnvDat() knows which dataset it fetched, so it records the step rather
   # than leaving it to be inferred. Worth trusting over the heuristics below: a
   # `dates` request of one date per month is genuinely indistinguishable from
   # monthly data by inspection, and guessing monthly would drop the day from the
   # match.
-  recorded <- attr(envDat, "datamatch_step")
+  recorded <- attr(x, "datamatch_step")
   if (!is.null(recorded) && recorded %in% c("day", "month", "year")) {
     return(recorded)
   }
 
-  times <- unique(sf::st_drop_geometry(envDat)[c("YEAR", "MONTH", "DAY")])
+  times <- unique(sf::st_drop_geometry(x)[c("YEAR", "MONTH", "DAY")])
 
   days_per_month <- tapply(times$DAY, paste(times$YEAR, times$MONTH), function(d) length(unique(d)))
   if (any(days_per_month > 1)) return("day")
@@ -163,28 +214,28 @@ detect_temporal_resolution <- function(envDat) {
   "month"
 }
 
-#' Rename a species dataset's time columns to YEAR/MONTH/DAY
+#' Rename a table's time columns to YEAR/MONTH/DAY
 #'
 #' Only the columns needed for the requested match keys are required, so monthly
 #' matching works on data that has no day column at all.
 #'
-#' @param speciesDat <sf object> species observation data
+#' @param dat <sf object> the table being matched
 #' @param match_keys <char> the standardized time columns needed, e.g. c("YEAR", "MONTH")
-#' @return `speciesDat` with its time columns renamed to YEAR/MONTH/DAY
+#' @return `dat` with its time columns renamed to YEAR/MONTH/DAY
 #' @keywords internal
-standardize_time_columns <- function(speciesDat, match_keys) {
+standardize_time_columns <- function(dat, match_keys) {
   # sf's select() method keeps the geometry column "sticky" regardless of the
   # select criteria, so it must be excluded here - otherwise the rename() below
   # would rename the geometry column itself and corrupt the sf object's tracked
   # geometry-column name.
-  geom_col <- attr(speciesDat, "sf_column")
+  geom_col <- attr(dat, "sf_column")
 
   for (key in match_keys) {
-    if (key %in% names(speciesDat)) next
+    if (key %in% names(dat)) next
 
     prefix <- tolower(key)
     candidates <- setdiff(
-      names(speciesDat |> dplyr::select(dplyr::starts_with(prefix, ignore.case = TRUE))),
+      names(dat |> dplyr::select(dplyr::starts_with(prefix, ignore.case = TRUE))),
       geom_col
     )
     # An exact match wins over a mere prefix match, so a dataset carrying both
@@ -195,17 +246,17 @@ standardize_time_columns <- function(speciesDat, match_keys) {
     }
 
     if (length(candidates) == 0) {
-      stop("speciesDat has no column for '", key, "' (looked for names starting with '",
+      stop("`dat` has no column for '", key, "' (looked for names starting with '",
            prefix, "'). It is required to match at this temporal resolution.",
            call. = FALSE)
     }
     if (length(candidates) > 1) {
-      stop("speciesDat has multiple candidate '", key, "' columns: ",
+      stop("`dat` has multiple candidate '", key, "' columns: ",
            paste(candidates, collapse = ", "),
            ". Rename the intended one to '", key, "'.", call. = FALSE)
     }
-    names(speciesDat)[names(speciesDat) == candidates] <- key
+    names(dat)[names(dat) == candidates] <- key
   }
 
-  speciesDat
+  dat
 }
diff --git a/R/plot.R b/R/plot.R
index 893726c..82c691d 100644
--- a/R/plot.R
+++ b/R/plot.R
@@ -223,7 +223,7 @@ plot_series <- function(env_dat, vars = NULL, fun = mean, spread = TRUE, ...) {
 #' @return the plotted values, invisibly
 #' @examples
 #' \dontrun{
-#' matched <- matchData(speciesDat = observations, envDat = env)
+#' matched <- matchData(observations, env)
 #'
 #' plot_matched(matched, "SST")
 #' # Open circles are observations that matched nothing.
diff --git a/README.Rmd b/README.Rmd
index de68c24..b876af4 100644
--- a/README.Rmd
+++ b/README.Rmd
@@ -61,7 +61,7 @@ The goal of datamatch is to pull environmental data from Copernicus Marine Servi
 **Putting it to use**
 
 - [Putting it together](#putting-it-together) — a full worked example, four sources onto one table
-- [Matching to observations](#matching-to-observations)
+- [Matching](#matching) — a general spatiotemporal join, not just observations
 - [Troubleshooting](#troubleshooting) — what the error messages mean
 - [Related packages](#related-packages)
 
@@ -121,7 +121,7 @@ env <- accessEnvDat(
   bounding_box = list(xmin = -76, xmax = -65, ymin = 35, ymax = 45)
 )
 
-matched <- matchData(speciesDat = observations, envDat = env)
+matched <- matchData(observations, env)
 ```
 
 `variable_dictionary()` lists what is available.
@@ -149,8 +149,8 @@ phys <- accessEnvDat(vars = c("SST", "SSS", "MLD"),
 bio  <- accessEnvDat(vars = c("CHL", "NO3"),
                      years = 2003:2017, months = 1:12, bounding_box = bb)
 
-matched <- matchData(speciesDat = observations, envDat = phys)
-matched <- matchData(speciesDat = matched,      envDat = bio)
+matched <- matchData(observations, phys)
+matched <- matchData(matched, bio)
 ```
 
 `matchData()` chains. Each call adds that product's columns and leaves the row
@@ -208,7 +208,7 @@ describe them. Take the dates from the observations themselves:
 env <- accessEnvDat(vars = "SST", dates = unique(observations$date),
                     bounding_box = bb)
 
-matched <- matchData(speciesDat = observations, envDat = env)
+matched <- matchData(observations, env)
 ```
 
 `YYYYMMDD` strings, `YYYY-MM-DD` strings, and `Date` objects are all accepted.
@@ -631,7 +631,7 @@ hides the difference between a uniformly warm month and one that is warm inshore
 and cold offshore.
 
 ```{r plot-matched, eval = FALSE}
-matched <- matchData(speciesDat = observations, envDat = env)
+matched <- matchData(observations, env)
 
 plot_matched(matched[matched$MONTH == 7, ], "SST")
 ```
@@ -873,8 +873,8 @@ bio  <- accessEnvDat(vars = c("CHL", "NO3"),
 bathy <- fetch_bathymetry(bounding_box = bb)
 
 # 3. Join each source to the observations in turn.
-matched <- matchData(speciesDat = observations, envDat = phys)
-matched <- matchData(speciesDat = matched,      envDat = bio)
+matched <- matchData(observations, phys)
+matched <- matchData(matched, bio)
 
 matched <- attach_bathymetry(matched, bathy, c("DEPTH", "SLOPE", "TPI"))
 matched <- attach_climate_index(matched, c("NAO", "LCR"))
@@ -917,20 +917,35 @@ rather than the observations. Regrid first, then match:
 phys_coarse <- upscale_grid(phys, to = bio)    # 0.083° onto the 0.25° BGC grid
 ```
 
-## Matching to observations
+## Matching
 
-`matchData()` joins environmental data to species observations at the
-environmental data's own temporal resolution, inferred from its time steps. This
-matters for monthly products: a monthly mean carries one time step per month
-while observations fall on arbitrary days, so matching on exact dates would
-match nothing.
+`matchData(dat, source)` joins each row of `dat` to the nearest feature of
+`source` within the same time period, and returns `dat` with `source`'s columns
+added.
 
 ```{r match, eval = FALSE}
-matched <- matchData(speciesDat = observations, envDat = env)
+matched <- matchData(observations, env)
 ```
 
-Observations falling in a period with no environmental data are returned with
-`NA` values and a warning naming the periods, rather than being dropped silently.
+**Neither side has to be observations or environmental data.** It is a
+spatiotemporal nearest-feature join between two `sf` point objects carrying
+`YEAR`/`MONTH`/`DAY`, so it works equally for stations against a covariate grid,
+tag positions against a model field, moorings against satellite retrievals, or
+one gridded product against another. The arguments are named `dat` and `source`
+for that reason.
+
+Matching happens at **`source`'s** temporal resolution, inferred from its time
+steps. That matters for monthly products: a monthly mean carries one time step
+per month while observations fall on arbitrary days, so matching on exact dates
+would match nothing. Pass `temporal_resolution` to override.
+
+Rows falling in a period `source` does not cover are returned with `NA` and a
+warning naming the periods, rather than being dropped silently. One row out per
+row in, always. A `source` column whose name collides with one already in `dat`
+is suffixed `.matched`, so nothing of yours is overwritten.
+
+> The arguments used to be `speciesDat` and `envDat`. Those still work and warn;
+> they will be removed in a later version.
 
 ## Troubleshooting
 
diff --git a/README.md b/README.md
index d5f39f2..6a04a24 100644
--- a/README.md
+++ b/README.md
@@ -54,7 +54,7 @@ The goal of datamatch is to pull environmental data from Copernicus Marine Servi
 **Putting it to use**
 
 - [Putting it together](#putting-it-together) — a full worked example, four sources onto one table
-- [Matching to observations](#matching-to-observations)
+- [Matching](#matching) — a general spatiotemporal join, not just observations
 - [Troubleshooting](#troubleshooting) — what the error messages mean
 - [Related packages](#related-packages)
 
@@ -115,7 +115,7 @@ env <- accessEnvDat(
   bounding_box = list(xmin = -76, xmax = -65, ymin = 35, ymax = 45)
 )
 
-matched <- matchData(speciesDat = observations, envDat = env)
+matched <- matchData(observations, env)
 ```
 
 `variable_dictionary()` lists what is available.
@@ -145,8 +145,8 @@ phys <- accessEnvDat(vars = c("SST", "SSS", "MLD"),
 bio  <- accessEnvDat(vars = c("CHL", "NO3"),
                      years = 2003:2017, months = 1:12, bounding_box = bb)
 
-matched <- matchData(speciesDat = observations, envDat = phys)
-matched <- matchData(speciesDat = matched,      envDat = bio)
+matched <- matchData(observations, phys)
+matched <- matchData(matched, bio)
 ```
 
 `matchData()` chains. Each call adds that product's columns and leaves the row
@@ -207,7 +207,7 @@ describe them. Take the dates from the observations themselves:
 env <- accessEnvDat(vars = "SST", dates = unique(observations$date),
                     bounding_box = bb)
 
-matched <- matchData(speciesDat = observations, envDat = env)
+matched <- matchData(observations, env)
 ```
 
 `YYYYMMDD` strings, `YYYY-MM-DD` strings, and `Date` objects are all accepted.
@@ -708,7 +708,7 @@ and cold offshore.
 
 
 ``` r
-matched <- matchData(speciesDat = observations, envDat = env)
+matched <- matchData(observations, env)
 
 plot_matched(matched[matched$MONTH == 7, ], "SST")
 ```
@@ -956,8 +956,8 @@ bio  <- accessEnvDat(vars = c("CHL", "NO3"),
 bathy <- fetch_bathymetry(bounding_box = bb)
 
 # 3. Join each source to the observations in turn.
-matched <- matchData(speciesDat = observations, envDat = phys)
-matched <- matchData(speciesDat = matched,      envDat = bio)
+matched <- matchData(observations, phys)
+matched <- matchData(matched, bio)
 
 matched <- attach_bathymetry(matched, bathy, c("DEPTH", "SLOPE", "TPI"))
 matched <- attach_climate_index(matched, c("NAO", "LCR"))
@@ -1002,21 +1002,36 @@ rather than the observations. Regrid first, then match:
 phys_coarse <- upscale_grid(phys, to = bio)    # 0.083° onto the 0.25° BGC grid
 ```
 
-## Matching to observations
+## Matching
 
-`matchData()` joins environmental data to species observations at the
-environmental data's own temporal resolution, inferred from its time steps. This
-matters for monthly products: a monthly mean carries one time step per month
-while observations fall on arbitrary days, so matching on exact dates would
-match nothing.
+`matchData(dat, source)` joins each row of `dat` to the nearest feature of
+`source` within the same time period, and returns `dat` with `source`'s columns
+added.
 
 
 ``` r
-matched <- matchData(speciesDat = observations, envDat = env)
+matched <- matchData(observations, env)
 ```
 
-Observations falling in a period with no environmental data are returned with
-`NA` values and a warning naming the periods, rather than being dropped silently.
+**Neither side has to be observations or environmental data.** It is a
+spatiotemporal nearest-feature join between two `sf` point objects carrying
+`YEAR`/`MONTH`/`DAY`, so it works equally for stations against a covariate grid,
+tag positions against a model field, moorings against satellite retrievals, or
+one gridded product against another. The arguments are named `dat` and `source`
+for that reason.
+
+Matching happens at **`source`'s** temporal resolution, inferred from its time
+steps. That matters for monthly products: a monthly mean carries one time step
+per month while observations fall on arbitrary days, so matching on exact dates
+would match nothing. Pass `temporal_resolution` to override.
+
+Rows falling in a period `source` does not cover are returned with `NA` and a
+warning naming the periods, rather than being dropped silently. One row out per
+row in, always. A `source` column whose name collides with one already in `dat`
+is suffixed `.matched`, so nothing of yours is overwritten.
+
+> The arguments used to be `speciesDat` and `envDat`. Those still work and warn;
+> they will be removed in a later version.
 
 ## Troubleshooting
 
diff --git a/man/accessEnvDat.Rd b/man/accessEnvDat.Rd
index 2fb60a2..939e5ba 100644
--- a/man/accessEnvDat.Rd
+++ b/man/accessEnvDat.Rd
@@ -61,7 +61,7 @@ ten days ahead. See \code{\link[=forecast_variables]{forecast_variables()}} for
 forecast equivalent and how the identifiers differ.}
 }
 \value{
-envDat \if{html}{\out{<sf object>}} sf object containing requested environmental data from Copernicus Marine Service
+\if{html}{\out{<sf object>}} sf object containing requested environmental data from Copernicus Marine Service
 }
 \description{
 Downloads a Copernicus dataset over a bounding box and time range, and returns
diff --git a/man/detect_temporal_resolution.Rd b/man/detect_temporal_resolution.Rd
index 8d42de7..b0b06e7 100644
--- a/man/detect_temporal_resolution.Rd
+++ b/man/detect_temporal_resolution.Rd
@@ -2,12 +2,13 @@
 % Please edit documentation in R/matchData.R
 \name{detect_temporal_resolution}
 \alias{detect_temporal_resolution}
-\title{Infer the temporal resolution of environmental data}
+\title{Infer the temporal resolution of a set of time steps}
 \usage{
-detect_temporal_resolution(envDat)
+detect_temporal_resolution(x)
 }
 \arguments{
-\item{envDat}{\if{html}{\out{<sf object>}} environmental data with YEAR/MONTH/DAY columns}
+\item{x}{\if{html}{\out{<sf object>}} an object with YEAR/MONTH/DAY columns, typically the
+\code{source} side of a match}
 }
 \value{
 one of "day", "month", or "year"
diff --git a/man/matchData.Rd b/man/matchData.Rd
index e5508a1..1880b10 100644
--- a/man/matchData.Rd
+++ b/man/matchData.Rd
@@ -2,43 +2,82 @@
 % Please edit documentation in R/matchData.R
 \name{matchData}
 \alias{matchData}
-\title{Match environmental data to species occurrence data}
+\title{Match one set of points to another in space and time}
 \usage{
 matchData(
-  speciesDat,
-  envDat,
-  temporal_resolution = c("auto", "day", "month", "year")
+  dat,
+  source,
+  temporal_resolution = c("auto", "day", "month", "year"),
+  speciesDat = NULL,
+  envDat = NULL
 )
 }
 \arguments{
-\item{speciesDat}{\if{html}{\out{<sf object>}} species observation data (e.g., presence, density, count);
-must have spatial and temporal components. Needs
-year and month columns, plus a day column when
-matching at daily resolution.}
+\item{dat}{\if{html}{\out{<sf object>}} the points to add columns to: observations, stations,
+tag positions, anything with coordinates and time. Needs year and month
+columns, plus a day column when matching at daily resolution. Columns whose
+names begin with those words are recognised, so \code{Year} or \code{obs_month} work.}
 
-\item{envDat}{\if{html}{\out{<sf object>}} environmental data accessed using the datamatch::accessEnvDat
-function to be matched to the species observation data}
+\item{source}{\if{html}{\out{<sf object>}} the points to take values from, typically a grid
+from \code{\link[=accessEnvDat]{accessEnvDat()}}. Must carry \code{YEAR}/\code{MONTH}/\code{DAY}.}
 
-\item{temporal_resolution}{\if{html}{\out{<char>}} one of "auto" (default), "day", "month", or
-"year". "auto" infers the resolution from
-\code{envDat}'s own time steps.}
+\item{temporal_resolution}{\if{html}{\out{<char>}} one of \code{"auto"} (default), \code{"day"},
+\code{"month"}, or \code{"year"}. \code{"auto"} uses the step \code{accessEnvDat()} recorded on
+\code{source}, or infers it from \code{source}'s time steps.}
+
+\item{speciesDat, envDat}{deprecated names for \code{dat} and \code{source}. Still
+accepted, with a warning.}
 }
 \value{
-\if{html}{\out{<sf object>}} \code{speciesDat} with the matched environmental variables joined on,
-one row per input observation, plus LON/LAT coordinate columns.
-Observations in a period with no environmental data get NA
-for the environmental variables, and a warning is issued.
-An environmental variable whose name collides with a column
-already in \code{speciesDat} is suffixed \code{.env}.
+\if{html}{\out{<sf object>}} \code{dat} with \code{source}'s columns joined on, one row per input
+row, plus \code{LON}/\code{LAT} coordinate columns.
 }
 \description{
-Joins each species observation to the nearest environmental grid point within
-the same time period. The time period is the environmental data's own temporal
-resolution: daily products match on year/month/day, monthly products (e.g.
-Copernicus \code{...P1M-m} means) on year/month, and annual products on year alone.
+Joins each row of \code{dat} to the nearest feature of \code{source} within the same
+time period, and returns \code{dat} with \code{source}'s columns added.
 }
 \details{
-Matching at the environmental data's native resolution matters because a
-day-exact join against monthly data matches nothing - a monthly product carries
-one time step per month, while observations fall on arbitrary days.
+Neither side has to be species observations or environmental data. It is a
+spatiotemporal nearest-feature join between two \code{sf} point objects that carry
+\code{YEAR}/\code{MONTH}/\code{DAY} columns, so it works equally for stations against a
+covariate grid, tag positions against a model field, moorings against
+satellite retrievals, or one gridded product against another.
+}
+\section{Matching in time}{
+
+The time period is \code{source}'s own resolution: daily data matches on
+year/month/day, monthly data (Copernicus \code{...P1M-m} means, say) on
+year/month, and annual data on year alone.
+
+That matters because a day-exact join against monthly data matches nothing. A
+monthly product carries one time step per month, while observations fall on
+arbitrary days. \code{temporal_resolution} overrides the inference when the data
+cannot speak for itself.
+}
+
+\section{What is preserved}{
+
+One row out per row of \code{dat}, in the same order, whatever happens. A period
+\code{source} does not cover gives \code{NA} for its columns and a warning naming the
+periods, rather than dropping those rows — a silent change in row count is a
+worse outcome than a visible gap.
+
+\code{dat} keeps its own columns. One of \code{source}'s that collides with a name
+already in \code{dat} is suffixed \code{.matched}, so nothing of \code{dat}'s is overwritten
+or renamed.
+}
+
+\examples{
+\dontrun{
+env <- accessEnvDat(vars = "SST", years = 2010, months = 1:12, bounding_box = bb)
+
+matched <- matchData(observations, env)
+
+# Chains, so several sources land on one table
+matched <- matchData(matched, chlorophyll)
+}
+}
+\seealso{
+\code{\link[=accessEnvDat]{accessEnvDat()}} for the usual \code{source}, \code{\link[=attach_bathymetry]{attach_bathymetry()}} and
+\code{\link[=attach_climate_index]{attach_climate_index()}} for covariates that are not matched this way
 }
diff --git a/man/plot_matched.Rd b/man/plot_matched.Rd
index a70147c..a52d202 100644
--- a/man/plot_matched.Rd
+++ b/man/plot_matched.Rd
@@ -40,7 +40,7 @@ reappears:
 
 \examples{
 \dontrun{
-matched <- matchData(speciesDat = observations, envDat = env)
+matched <- matchData(observations, env)
 
 plot_matched(matched, "SST")
 # Open circles are observations that matched nothing.
diff --git a/man/standardize_time_columns.Rd b/man/standardize_time_columns.Rd
index ad99378..1cadb13 100644
--- a/man/standardize_time_columns.Rd
+++ b/man/standardize_time_columns.Rd
@@ -2,17 +2,17 @@
 % Please edit documentation in R/matchData.R
 \name{standardize_time_columns}
 \alias{standardize_time_columns}
-\title{Rename a species dataset's time columns to YEAR/MONTH/DAY}
+\title{Rename a table's time columns to YEAR/MONTH/DAY}
 \usage{
-standardize_time_columns(speciesDat, match_keys)
+standardize_time_columns(dat, match_keys)
 }
 \arguments{
-\item{speciesDat}{\if{html}{\out{<sf object>}} species observation data}
+\item{dat}{\if{html}{\out{<sf object>}} the table being matched}
 
 \item{match_keys}{\if{html}{\out{<char>}} the standardized time columns needed, e.g. c("YEAR", "MONTH")}
 }
 \value{
-\code{speciesDat} with its time columns renamed to YEAR/MONTH/DAY
+\code{dat} with its time columns renamed to YEAR/MONTH/DAY
 }
 \description{
 Only the columns needed for the requested match keys are required, so monthly
diff --git a/tests/testthat/test-matchData.R b/tests/testthat/test-matchData.R
index a166e6c..c73e167 100644
--- a/tests/testthat/test-matchData.R
+++ b/tests/testthat/test-matchData.R
@@ -1,7 +1,7 @@
 # Helpers -----------------------------------------------------------------
 
 # Species observations: points with known lon/lat and YEAR/MONTH/DAY columns.
-make_species_dat <- function(year_col = "YEAR", month_col = "MONTH", day_col = "DAY") {
+make_observations <- function(year_col = "YEAR", month_col = "MONTH", day_col = "DAY") {
   df <- data.frame(
     id    = 1:3,
     lon   = c(-70.0, -69.5, -69.0),
@@ -24,8 +24,8 @@ make_species_dat <- function(year_col = "YEAR", month_col = "MONTH", day_col = "
 # matching the two January species points below) with deliberately different
 # thetao values at the same locations, so a test can confirm matchData()
 # picks the value for the correct day rather than whichever day happens to
-# come first in envDat.
-make_env_dat <- function() {
+# come first in env.
+make_env <- function() {
   grid <- expand.grid(
     lon = c(-70.0, -69.5, -69.0),
     lat = c(42.0, 42.5, 43.0)
@@ -41,19 +41,19 @@ make_env_dat <- function() {
 # Tests ---------------------------------------------------------------------
 
 test_that("matchData renames YEAR/MONTH/DAY columns regardless of input naming", {
-  speciesDat <- make_species_dat(year_col = "Year", month_col = "Month", day_col = "Day")
-  envDat <- make_env_dat()
+  observations <- make_observations(year_col = "Year", month_col = "Month", day_col = "Day")
+  env <- make_env()
 
-  result <- matchData(speciesDat, envDat)
+  result <- matchData(observations, env)
 
   expect_true(all(c("YEAR", "MONTH", "DAY") %in% names(result)))
 })
 
 test_that("matchData joins the correct environmental value via nearest feature and matching day", {
-  speciesDat <- make_species_dat()
-  envDat <- make_env_dat()
+  observations <- make_observations()
+  env <- make_env()
 
-  result <- matchData(speciesDat, envDat)
+  result <- matchData(observations, env)
 
   # Species point 1: Jan 1 2020, sits exactly on env grid point 1 -> the Jan-1 value
   expect_equal(result$thetao[result$id == 1], 11)
@@ -67,19 +67,19 @@ test_that("matchData joins the correct environmental value via nearest feature a
 })
 
 test_that("matchData returns one row per species observation", {
-  speciesDat <- make_species_dat()
-  envDat <- make_env_dat()
+  observations <- make_observations()
+  env <- make_env()
 
-  result <- matchData(speciesDat, envDat)
+  result <- matchData(observations, env)
 
-  expect_equal(nrow(result), nrow(speciesDat))
+  expect_equal(nrow(result), nrow(observations))
 })
 
 test_that("matchData assigns LAT and LON correctly (not swapped)", {
-  speciesDat <- make_species_dat()
-  envDat <- make_env_dat()
+  observations <- make_observations()
+  env <- make_env()
 
-  result <- matchData(speciesDat, envDat)
+  result <- matchData(observations, env)
   row <- result[result$id == 1, ]
 
   expect_equal(row$LON, -70.0, tolerance = 1e-6)
@@ -87,12 +87,12 @@ test_that("matchData assigns LAT and LON correctly (not swapped)", {
 })
 
 test_that("matchData drops YEAR/MONTH duplication from the env side", {
-  speciesDat <- make_species_dat()
-  envDat <- make_env_dat()
+  observations <- make_observations()
+  env <- make_env()
 
-  result <- matchData(speciesDat, envDat)
+  result <- matchData(observations, env)
 
-  # YEAR/MONTH should appear exactly once each (from speciesDat), not duplicated
+  # YEAR/MONTH should appear exactly once each (from observations), not duplicated
   expect_equal(sum(names(result) == "YEAR"), 1)
   expect_equal(sum(names(result) == "MONTH"), 1)
 })
@@ -102,7 +102,7 @@ test_that("matchData drops YEAR/MONTH duplication from the env side", {
 # Monthly products (e.g. Copernicus "...P1M-m" means) carry one time step per
 # month, conventionally stamped on a single nominal day. Observations still fall
 # on arbitrary days, so a day-exact join would match nothing.
-make_monthly_env_dat <- function(nominal_day = 1) {
+make_monthly_env <- function(nominal_day = 1) {
   grid <- expand.grid(
     lon = c(-70.0, -69.5, -69.0),
     lat = c(42.0, 42.5, 43.0)
@@ -115,15 +115,15 @@ make_monthly_env_dat <- function(nominal_day = 1) {
 }
 
 test_that("detect_temporal_resolution reads resolution off the time steps", {
-  expect_equal(detect_temporal_resolution(make_env_dat()), "day")
-  expect_equal(detect_temporal_resolution(make_monthly_env_dat()), "month")
+  expect_equal(detect_temporal_resolution(make_env()), "day")
+  expect_equal(detect_temporal_resolution(make_monthly_env()), "month")
 })
 
 test_that("detect_temporal_resolution prefers month over year when ambiguous", {
   # One month of monthly data looks identical to one year of annual data. Falling
   # back to "month" keeps unmatched observations unmatched (and warned about),
   # rather than silently matching them to another month's time step.
-  one_month <- make_monthly_env_dat()
+  one_month <- make_monthly_env()
   one_month <- one_month[one_month$MONTH == 1, ]
   expect_equal(detect_temporal_resolution(one_month), "month")
 
@@ -137,14 +137,14 @@ test_that("detect_temporal_resolution prefers month over year when ambiguous", {
 })
 
 test_that("matchData matches monthly env data whose nominal day never matches observations", {
-  speciesDat <- make_species_dat()
+  observations <- make_observations()
   # Env data is stamped on day 15; observations are on days 1 and 15. A day-exact
   # join would drop both January-day-1 and February-day-1 observations entirely.
-  envDat <- make_monthly_env_dat(nominal_day = 15)
+  env <- make_monthly_env(nominal_day = 15)
 
-  result <- matchData(speciesDat, envDat)
+  result <- matchData(observations, env)
 
-  expect_equal(nrow(result), nrow(speciesDat))
+  expect_equal(nrow(result), nrow(observations))
   expect_false(any(is.na(result$thetao)))
   # Point 1: Jan, on grid point 1 -> January value at that location
   expect_equal(result$thetao[result$id == 1], 11)
@@ -153,62 +153,62 @@ test_that("matchData matches monthly env data whose nominal day never matches ob
 })
 
 test_that("matchData still matches per-day when env data is daily", {
-  speciesDat <- make_species_dat()
-  envDat <- make_env_dat()
+  observations <- make_observations()
+  env <- make_env()
 
   # Explicitly requesting month resolution against daily data is ambiguous by
   # design; auto-detection is what keeps daily data matching per day.
-  result <- matchData(speciesDat, envDat, temporal_resolution = "day")
+  result <- matchData(observations, env, temporal_resolution = "day")
 
   expect_equal(result$thetao[result$id == 2], 115)
 })
 
 test_that("matchData works on species data with no day column at monthly resolution", {
-  speciesDat <- make_species_dat()
-  speciesDat$DAY <- NULL
-  envDat <- make_monthly_env_dat()
+  observations <- make_observations()
+  observations$DAY <- NULL
+  env <- make_monthly_env()
 
-  result <- matchData(speciesDat, envDat)
+  result <- matchData(observations, env)
 
-  expect_equal(nrow(result), nrow(speciesDat))
+  expect_equal(nrow(result), nrow(observations))
   expect_false(any(is.na(result$thetao)))
 })
 
 test_that("matchData resolves an exact day column ahead of a prefix match", {
-  speciesDat <- make_species_dat()
-  speciesDat$dayofyear <- c(1, 15, 32)
-  envDat <- make_env_dat()
+  observations <- make_observations()
+  observations$dayofyear <- c(1, 15, 32)
+  env <- make_env()
 
   # Both "DAY" and "dayofyear" start with "day"; the exact match must win rather
   # than the lookup failing as ambiguous.
-  expect_no_error(matchData(speciesDat, envDat))
+  expect_no_error(matchData(observations, env))
 })
 
 test_that("matchData keeps observations in periods with no env data, as NA", {
-  speciesDat <- make_species_dat()
+  observations <- make_observations()
   # Env data covers January only; the February observation has no match.
-  envDat <- make_monthly_env_dat()
-  envDat <- envDat[envDat$MONTH == 1, ]
+  env <- make_monthly_env()
+  env <- env[env$MONTH == 1, ]
 
-  expect_warning(result <- matchData(speciesDat, envDat), "No environmental data")
+  expect_warning(result <- matchData(observations, env), "No data in `source`")
 
-  expect_equal(nrow(result), nrow(speciesDat))
+  expect_equal(nrow(result), nrow(observations))
   expect_true(is.na(result$thetao[result$id == 3]))
   expect_false(is.na(result$thetao[result$id == 1]))
 })
 
-test_that("matchData does not depend on the order periods appear in speciesDat", {
-  speciesDat <- make_species_dat()
-  envDat <- make_env_dat()
+test_that("matchData does not depend on the order periods appear in observations", {
+  observations <- make_observations()
+  env <- make_env()
 
   # Reversing row order puts the chronologically last period first. The previous
   # implementation initialized its accumulator only on the chronologically first
   # period, so this ordering made it fail outright.
-  reversed <- speciesDat[rev(seq_len(nrow(speciesDat))), ]
+  reversed <- observations[rev(seq_len(nrow(observations))), ]
 
-  result <- matchData(reversed, envDat)
+  result <- matchData(reversed, env)
 
-  expect_equal(nrow(result), nrow(speciesDat))
+  expect_equal(nrow(result), nrow(observations))
   expect_equal(result$thetao[result$id == 2], 115)
 })
 
@@ -223,13 +223,13 @@ test_that("a missing CRS is named, on whichever side it is missing", {
   # neither which object nor what to do about it.
   expect_error(
     matchData(sf::st_as_sf(obs, coords = c("lon", "lat")), env),
-    "speciesDat has no coordinate reference system")
+    "`dat` has no coordinate reference system")
 
   env_no_crs <- env
   sf::st_crs(env_no_crs) <- NA
   expect_error(
     matchData(sf::st_as_sf(obs, coords = c("lon", "lat"), crs = 4326), env_no_crs),
-    "envDat has no coordinate reference system")
+    "`source` has no coordinate reference system")
 })
 
 test_that("projected observations match the same cells as geographic ones", {
@@ -246,3 +246,63 @@ test_that("projected observations match the same cells as geographic ones", {
   expect_equal(suppressWarnings(matchData(projected, env))$SST,
                suppressWarnings(matchData(geographic, env))$SST)
 })
+
+test_that("the deprecated argument names still work, with a warning", {
+  # taupatch and any script written against the old signature call these by
+  # name. Breaking them silently would be worse than carrying the shim.
+  observations <- make_observations()
+  env <- make_env()
+
+  # Both names warn, so both have to be caught or the second escapes the test.
+  expect_warning(
+    expect_warning(matchData(speciesDat = observations, envDat = env),
+                   "`speciesDat` is now `dat`"),
+    "`envDat` is now `source`")
+
+  old <- suppressWarnings(matchData(speciesDat = observations, envDat = env))
+  new <- matchData(dat = observations, source = env)
+
+  expect_equal(sf::st_drop_geometry(old), sf::st_drop_geometry(new))
+})
+
+test_that("the new names work positionally and by name", {
+  observations <- make_observations()
+  env <- make_env()
+
+  expect_equal(sf::st_drop_geometry(matchData(observations, env)),
+               sf::st_drop_geometry(matchData(dat = observations, source = env)))
+  expect_silent(matchData(observations, env))
+})
+
+test_that("a colliding column is suffixed rather than overwriting the caller's", {
+  # The suffix is ".matched" now: the join is no longer specific to
+  # environmental data, so ".env" described only one use of it.
+  observations <- make_observations()
+  env <- make_env()
+  observations$thetao <- seq_len(nrow(observations)) * 100
+
+  result <- matchData(observations, env)
+
+  expect_true(all(c("thetao", "thetao.matched") %in% names(result)))
+  # The caller's own column is untouched.
+  expect_equal(result$thetao, seq_len(nrow(observations)) * 100)
+})
+
+test_that("neither side has to be observations or a covariate grid", {
+  # The generalisation this rename is about: two gridded products matched to
+  # each other, with nothing species-shaped involved.
+  grid_a <- sf::st_as_sf(
+    data.frame(x = rep(seq(-70, -69, by = 0.5), 2), y = rep(c(42, 43), each = 3),
+               SST = 1:6, YEAR = 2010L, MONTH = 1L, DAY = 1L),
+    coords = c("x", "y"), crs = 4326)
+  grid_b <- sf::st_as_sf(
+    data.frame(x = c(-69.75, -69.25), y = c(42, 43),
+               CHL = c(0.5, 0.9), YEAR = 2010L, MONTH = 1L, DAY = 1L),
+    coords = c("x", "y"), crs = 4326)
+
+  result <- matchData(grid_a, grid_b)
+
+  expect_equal(nrow(result), nrow(grid_a))
+  expect_true(all(c("SST", "CHL") %in% names(result)))
+  expect_false(anyNA(result$CHL))
+})